{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Predictive Maintenance using Deep Learning\n",
    "## Fault Classification using deep learning with Keras\n",
    "\n",
    "#### Author Nagdev Amruthnath\n",
    "Date: 1/10/2019\n",
    "\n",
    "##### Citation Info\n",
    "If you are using this for your research, please use the following for citation. \n",
    "\n",
    "Amruthnath, Nagdev, and Tarun Gupta. \"A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance.\" In 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), pp. 355-361. IEEE, 2018.\n",
    "\n",
    "##### Disclaimer\n",
    "This is a tutorial for performing fault detection using machine learning. You this code at your own risk. I do not gurantee that this would work as shown below. If you have any suggestions please branch this project.\n",
    "\n",
    "## Introduction\n",
    "This is the first of four part demostration series of using machine learning for predictive maintenance.   \n",
    "\n",
    "The area of predictive maintenance has taken a lot of prominence in the last couple of years due to various reasons. With new algorithms and methodologies growing across different learning methods, it has remained a challenge for industries to adopt which method is fit, robust and provide most accurate detection. One the most common learning approaches used today for fault diagnosis is supervised learning. This is wholly based on the predictor variable and response variable. In this tutorial, we will be looking into deep learning models for fault classification using keras package in R.\n",
    "\n",
    "\n",
    "## Load libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-10T19:23:37.732876Z",
     "start_time": "2019-01-10T19:23:37.657Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: lattice\n",
      "Loading required package: ggplot2\n",
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n"
     ]
    }
   ],
   "source": [
    "options(warn=-1)\n",
    "#install.packages(\"keras\")\n",
    "# load libraries\n",
    "library(keras)\n",
    "library(caret)\n",
    "library(dplyr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using virtual environment '~/.virtualenvs/r-reticulate' ...\n",
      "\n",
      "Installation complete.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# install keras\n",
    "install_keras()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "Here we are using data from a bench press. There are total of four different states in this machine and they are split into four different csv files. We need to load the data first. In the data time represents the time between samples, ax is the acceleration on x axis, ay is the acceleration on y axis, az is the acceleration on z axis and at is the G's. The data was collected at sample rate of 100hz.   \n",
    "\n",
    "Four different states of the machine were collected  \n",
    "1. Nothing attached to drill press\n",
    "2. Wooden base attached to drill press\n",
    "3. Imbalance created by adding weight to one end of wooden base\n",
    "4. Imbalacne created by adding weight to two ends of wooden base."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-10T19:23:40.522397Z",
     "start_time": "2019-01-10T19:23:40.029Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>time</th><th scope=col>ax</th><th scope=col>ay</th><th scope=col>az</th><th scope=col>aT</th><th scope=col>y</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.002</td><td>-0.3246</td><td> 0.2748</td><td> 0.1502</td><td>0.451</td><td>1</td></tr>\n",
       "\t<tr><td>0.009</td><td> 0.6020</td><td>-0.1900</td><td>-0.3227</td><td>0.709</td><td>1</td></tr>\n",
       "\t<tr><td>0.019</td><td> 0.9787</td><td> 0.3258</td><td> 0.0124</td><td>1.032</td><td>1</td></tr>\n",
       "\t<tr><td>0.027</td><td> 0.6141</td><td>-0.4179</td><td> 0.0471</td><td>0.744</td><td>1</td></tr>\n",
       "\t<tr><td>0.038</td><td>-0.3218</td><td>-0.6389</td><td>-0.4259</td><td>0.833</td><td>1</td></tr>\n",
       "\t<tr><td>0.047</td><td>-0.3607</td><td> 0.1332</td><td>-0.1291</td><td>0.406</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 6\n",
       "\\begin{tabular}{r|llllll}\n",
       " time & ax & ay & az & aT & y\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.002 & -0.3246 &  0.2748 &  0.1502 & 0.451 & 1\\\\\n",
       "\t 0.009 &  0.6020 & -0.1900 & -0.3227 & 0.709 & 1\\\\\n",
       "\t 0.019 &  0.9787 &  0.3258 &  0.0124 & 1.032 & 1\\\\\n",
       "\t 0.027 &  0.6141 & -0.4179 &  0.0471 & 0.744 & 1\\\\\n",
       "\t 0.038 & -0.3218 & -0.6389 & -0.4259 & 0.833 & 1\\\\\n",
       "\t 0.047 & -0.3607 &  0.1332 & -0.1291 & 0.406 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 6\n",
       "\n",
       "| time &lt;dbl&gt; | ax &lt;dbl&gt; | ay &lt;dbl&gt; | az &lt;dbl&gt; | aT &lt;dbl&gt; | y &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 0.002 | -0.3246 |  0.2748 |  0.1502 | 0.451 | 1 |\n",
       "| 0.009 |  0.6020 | -0.1900 | -0.3227 | 0.709 | 1 |\n",
       "| 0.019 |  0.9787 |  0.3258 |  0.0124 | 1.032 | 1 |\n",
       "| 0.027 |  0.6141 | -0.4179 |  0.0471 | 0.744 | 1 |\n",
       "| 0.038 | -0.3218 | -0.6389 | -0.4259 | 0.833 | 1 |\n",
       "| 0.047 | -0.3607 |  0.1332 | -0.1291 | 0.406 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  time  ax      ay      az      aT    y\n",
       "1 0.002 -0.3246  0.2748  0.1502 0.451 1\n",
       "2 0.009  0.6020 -0.1900 -0.3227 0.709 1\n",
       "3 0.019  0.9787  0.3258  0.0124 1.032 1\n",
       "4 0.027  0.6141 -0.4179  0.0471 0.744 1\n",
       "5 0.038 -0.3218 -0.6389 -0.4259 0.833 1\n",
       "6 0.047 -0.3607  0.1332 -0.1291 0.406 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setwd(\"/home\")\n",
    "#read csv files\n",
    "file1 = read.csv(\"dry run.csv\", sep=\",\", header =T)\n",
    "file2 = read.csv(\"base.csv\", sep=\",\", header =T)\n",
    "file3 = read.csv(\"imbalance 1.csv\", sep=\",\", header =T)\n",
    "file4 = read.csv(\"imbalance 2.csv\", sep=\",\", header =T)\n",
    "\n",
    "#Add labels to data\n",
    "file1$y = 1\n",
    "file2$y = 2\n",
    "file3$y = 3\n",
    "file4$y = 4\n",
    "\n",
    "#view top rows of data\n",
    "head(file1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the summary of each file using summary function in R. Below, we can observe that 66 seconds long data is available. We also have min, max and mean for each of the variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-10T19:23:42.029410Z",
     "start_time": "2019-01-10T19:23:41.958Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      time              ax                 ay                  az         \n",
       " Min.   : 0.002   Min.   :-2.11880   Min.   :-2.143600   Min.   :-4.1744  \n",
       " 1st Qu.:16.507   1st Qu.:-0.41478   1st Qu.:-0.625250   1st Qu.:-0.7359  \n",
       " Median :33.044   Median : 0.02960   Median :-0.022050   Median :-0.1468  \n",
       " Mean   :33.037   Mean   : 0.01233   Mean   : 0.008697   Mean   :-0.1021  \n",
       " 3rd Qu.:49.535   3rd Qu.: 0.46003   3rd Qu.: 0.641700   3rd Qu.: 0.4298  \n",
       " Max.   :66.033   Max.   : 2.09620   Max.   : 2.003000   Max.   : 4.9466  \n",
       "       aT              y    \n",
       " Min.   :0.032   Min.   :1  \n",
       " 1st Qu.:0.848   1st Qu.:1  \n",
       " Median :1.169   Median :1  \n",
       " Mean   :1.277   Mean   :1  \n",
       " 3rd Qu.:1.579   3rd Qu.:1  \n",
       " Max.   :5.013   Max.   :1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summary of each file\n",
    "summary(file1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Aggregration and feature extraction\n",
    "Here, the data is aggregated by 1 minute and features are extracted. Features are extracted to reduce the dimension of the data and only storing the representation of the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-10T19:23:46.121459Z",
     "start_time": "2019-01-10T19:23:45.638Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 6 × 22</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>group</th><th scope=col>ax_mean</th><th scope=col>ax_sd</th><th scope=col>ax_min</th><th scope=col>ax_max</th><th scope=col>ax_median</th><th scope=col>ay_mean</th><th scope=col>ay_sd</th><th scope=col>ay_min</th><th scope=col>ay_may</th><th scope=col>⋯</th><th scope=col>az_sd</th><th scope=col>az_min</th><th scope=col>az_maz</th><th scope=col>az_median</th><th scope=col>aT_mean</th><th scope=col>aT_sd</th><th scope=col>aT_min</th><th scope=col>aT_maT</th><th scope=col>aT_median</th><th scope=col>y</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>-0.038164706</td><td>0.6594686</td><td>-1.2587</td><td>1.3821</td><td>-0.0955</td><td>-0.0682627451</td><td>0.7506785</td><td>-1.3892</td><td>1.6418</td><td>⋯</td><td>0.9845115</td><td>-2.6753</td><td>2.7507</td><td> 0.0254</td><td>1.273216</td><td>0.5830149</td><td>0.400</td><td>3.029</td><td>1.0770</td><td>1</td></tr>\n",
       "\t<tr><td>1</td><td>-0.005806122</td><td>0.6325808</td><td>-1.6194</td><td>1.1943</td><td>-0.0015</td><td> 0.0037908163</td><td>0.7819044</td><td>-1.5625</td><td>1.5428</td><td>⋯</td><td>0.9252188</td><td>-3.0774</td><td>2.7158</td><td>-0.2121</td><td>1.263622</td><td>0.5448447</td><td>0.410</td><td>3.197</td><td>1.1375</td><td>1</td></tr>\n",
       "\t<tr><td>2</td><td> 0.069845455</td><td>0.6665500</td><td>-1.4554</td><td>1.4667</td><td> 0.1070</td><td> 0.0744333333</td><td>0.8022922</td><td>-1.4800</td><td>1.7951</td><td>⋯</td><td>0.9293866</td><td>-1.8205</td><td>2.4862</td><td>-0.1512</td><td>1.298364</td><td>0.5131552</td><td>0.255</td><td>2.644</td><td>1.2830</td><td>1</td></tr>\n",
       "\t<tr><td>3</td><td> 0.011552525</td><td>0.5511310</td><td>-1.9254</td><td>1.2034</td><td> 0.0675</td><td> 0.0008262626</td><td>0.7894209</td><td>-2.0042</td><td>1.5577</td><td>⋯</td><td>0.8893505</td><td>-2.1562</td><td>3.2355</td><td>-0.1672</td><td>1.203848</td><td>0.5125826</td><td>0.393</td><td>3.322</td><td>1.1180</td><td>1</td></tr>\n",
       "\t<tr><td>4</td><td> 0.046688119</td><td>0.6426574</td><td>-1.7805</td><td>1.4837</td><td> 0.0836</td><td>-0.0177594059</td><td>0.7510811</td><td>-1.6629</td><td>1.4369</td><td>⋯</td><td>0.9265720</td><td>-1.8515</td><td>3.5451</td><td>-0.1741</td><td>1.226267</td><td>0.5824608</td><td>0.313</td><td>3.597</td><td>1.1720</td><td>1</td></tr>\n",
       "\t<tr><td>5</td><td> 0.006678788</td><td>0.5780957</td><td>-1.4719</td><td>1.4355</td><td> 0.0536</td><td> 0.0013626263</td><td>0.7812245</td><td>-1.6293</td><td>1.6362</td><td>⋯</td><td>0.9091516</td><td>-2.5561</td><td>2.9196</td><td>-0.2588</td><td>1.209515</td><td>0.5664847</td><td>0.336</td><td>3.035</td><td>1.1590</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 22\n",
       "\\begin{tabular}{r|llllllllllllllllllllll}\n",
       " group & ax\\_mean & ax\\_sd & ax\\_min & ax\\_max & ax\\_median & ay\\_mean & ay\\_sd & ay\\_min & ay\\_may & ay\\_median & az\\_mean & az\\_sd & az\\_min & az\\_maz & az\\_median & aT\\_mean & aT\\_sd & aT\\_min & aT\\_maT & aT\\_median & y\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t 0 & -0.038164706 & 0.6594686 & -1.2587 & 1.3821 & -0.0955 & -0.0682627451 & 0.7506785 & -1.3892 & 1.6418 & -0.19000 & -0.13803333 & 0.9845115 & -2.6753 & 2.7507 &  0.0254 & 1.273216 & 0.5830149 & 0.400 & 3.029 & 1.0770 & 1\\\\\n",
       "\t 1 & -0.005806122 & 0.6325808 & -1.6194 & 1.1943 & -0.0015 &  0.0037908163 & 0.7819044 & -1.5625 & 1.5428 &  0.01005 & -0.20496837 & 0.9252188 & -3.0774 & 2.7158 & -0.2121 & 1.263622 & 0.5448447 & 0.410 & 3.197 & 1.1375 & 1\\\\\n",
       "\t 2 &  0.069845455 & 0.6665500 & -1.4554 & 1.4667 &  0.1070 &  0.0744333333 & 0.8022922 & -1.4800 & 1.7951 &  0.11860 & -0.06405354 & 0.9293866 & -1.8205 & 2.4862 & -0.1512 & 1.298364 & 0.5131552 & 0.255 & 2.644 & 1.2830 & 1\\\\\n",
       "\t 3 &  0.011552525 & 0.5511310 & -1.9254 & 1.2034 &  0.0675 &  0.0008262626 & 0.7894209 & -2.0042 & 1.5577 & -0.00270 & -0.09287879 & 0.8893505 & -2.1562 & 3.2355 & -0.1672 & 1.203848 & 0.5125826 & 0.393 & 3.322 & 1.1180 & 1\\\\\n",
       "\t 4 &  0.046688119 & 0.6426574 & -1.7805 & 1.4837 &  0.0836 & -0.0177594059 & 0.7510811 & -1.6629 & 1.4369 & -0.02530 & -0.13990000 & 0.9265720 & -1.8515 & 3.5451 & -0.1741 & 1.226267 & 0.5824608 & 0.313 & 3.597 & 1.1720 & 1\\\\\n",
       "\t 5 &  0.006678788 & 0.5780957 & -1.4719 & 1.4355 &  0.0536 &  0.0013626263 & 0.7812245 & -1.6293 & 1.6362 & -0.09910 & -0.16540000 & 0.9091516 & -2.5561 & 2.9196 & -0.2588 & 1.209515 & 0.5664847 & 0.336 & 3.035 & 1.1590 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 22\n",
       "\n",
       "| group &lt;fct&gt; | ax_mean &lt;dbl&gt; | ax_sd &lt;dbl&gt; | ax_min &lt;dbl&gt; | ax_max &lt;dbl&gt; | ax_median &lt;dbl&gt; | ay_mean &lt;dbl&gt; | ay_sd &lt;dbl&gt; | ay_min &lt;dbl&gt; | ay_may &lt;dbl&gt; | ⋯ ⋯ | az_sd &lt;dbl&gt; | az_min &lt;dbl&gt; | az_maz &lt;dbl&gt; | az_median &lt;dbl&gt; | aT_mean &lt;dbl&gt; | aT_sd &lt;dbl&gt; | aT_min &lt;dbl&gt; | aT_maT &lt;dbl&gt; | aT_median &lt;dbl&gt; | y &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0 | -0.038164706 | 0.6594686 | -1.2587 | 1.3821 | -0.0955 | -0.0682627451 | 0.7506785 | -1.3892 | 1.6418 | ⋯ | 0.9845115 | -2.6753 | 2.7507 |  0.0254 | 1.273216 | 0.5830149 | 0.400 | 3.029 | 1.0770 | 1 |\n",
       "| 1 | -0.005806122 | 0.6325808 | -1.6194 | 1.1943 | -0.0015 |  0.0037908163 | 0.7819044 | -1.5625 | 1.5428 | ⋯ | 0.9252188 | -3.0774 | 2.7158 | -0.2121 | 1.263622 | 0.5448447 | 0.410 | 3.197 | 1.1375 | 1 |\n",
       "| 2 |  0.069845455 | 0.6665500 | -1.4554 | 1.4667 |  0.1070 |  0.0744333333 | 0.8022922 | -1.4800 | 1.7951 | ⋯ | 0.9293866 | -1.8205 | 2.4862 | -0.1512 | 1.298364 | 0.5131552 | 0.255 | 2.644 | 1.2830 | 1 |\n",
       "| 3 |  0.011552525 | 0.5511310 | -1.9254 | 1.2034 |  0.0675 |  0.0008262626 | 0.7894209 | -2.0042 | 1.5577 | ⋯ | 0.8893505 | -2.1562 | 3.2355 | -0.1672 | 1.203848 | 0.5125826 | 0.393 | 3.322 | 1.1180 | 1 |\n",
       "| 4 |  0.046688119 | 0.6426574 | -1.7805 | 1.4837 |  0.0836 | -0.0177594059 | 0.7510811 | -1.6629 | 1.4369 | ⋯ | 0.9265720 | -1.8515 | 3.5451 | -0.1741 | 1.226267 | 0.5824608 | 0.313 | 3.597 | 1.1720 | 1 |\n",
       "| 5 |  0.006678788 | 0.5780957 | -1.4719 | 1.4355 |  0.0536 |  0.0013626263 | 0.7812245 | -1.6293 | 1.6362 | ⋯ | 0.9091516 | -2.5561 | 2.9196 | -0.2588 | 1.209515 | 0.5664847 | 0.336 | 3.035 | 1.1590 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  group ax_mean      ax_sd     ax_min  ax_max ax_median ay_mean       ay_sd    \n",
       "1 0     -0.038164706 0.6594686 -1.2587 1.3821 -0.0955   -0.0682627451 0.7506785\n",
       "2 1     -0.005806122 0.6325808 -1.6194 1.1943 -0.0015    0.0037908163 0.7819044\n",
       "3 2      0.069845455 0.6665500 -1.4554 1.4667  0.1070    0.0744333333 0.8022922\n",
       "4 3      0.011552525 0.5511310 -1.9254 1.2034  0.0675    0.0008262626 0.7894209\n",
       "5 4      0.046688119 0.6426574 -1.7805 1.4837  0.0836   -0.0177594059 0.7510811\n",
       "6 5      0.006678788 0.5780957 -1.4719 1.4355  0.0536    0.0013626263 0.7812245\n",
       "  ay_min  ay_may ⋯ az_sd     az_min  az_maz az_median aT_mean  aT_sd     aT_min\n",
       "1 -1.3892 1.6418 ⋯ 0.9845115 -2.6753 2.7507  0.0254   1.273216 0.5830149 0.400 \n",
       "2 -1.5625 1.5428 ⋯ 0.9252188 -3.0774 2.7158 -0.2121   1.263622 0.5448447 0.410 \n",
       "3 -1.4800 1.7951 ⋯ 0.9293866 -1.8205 2.4862 -0.1512   1.298364 0.5131552 0.255 \n",
       "4 -2.0042 1.5577 ⋯ 0.8893505 -2.1562 3.2355 -0.1672   1.203848 0.5125826 0.393 \n",
       "5 -1.6629 1.4369 ⋯ 0.9265720 -1.8515 3.5451 -0.1741   1.226267 0.5824608 0.313 \n",
       "6 -1.6293 1.6362 ⋯ 0.9091516 -2.5561 2.9196 -0.2588   1.209515 0.5664847 0.336 \n",
       "  aT_maT aT_median y\n",
       "1 3.029  1.0770    1\n",
       "2 3.197  1.1375    1\n",
       "3 2.644  1.2830    1\n",
       "4 3.322  1.1180    1\n",
       "5 3.597  1.1720    1\n",
       "6 3.035  1.1590    1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file1$group = as.factor(round(file1$time))\n",
    "file2$group = as.factor(round(file2$time))\n",
    "file3$group = as.factor(round(file3$time))\n",
    "file4$group = as.factor(round(file4$time))\n",
    "#(file1,20)\n",
    "\n",
    "#list of all files\n",
    "files = list(file1, file2, file3, file4)\n",
    "\n",
    "#loop through all files and combine\n",
    "features = NULL\n",
    "for (i in 1:4){\n",
    "res = files[[i]] %>%\n",
    "    group_by(group) %>%\n",
    "    summarize(ax_mean = mean(ax),\n",
    "              ax_sd = sd(ax),\n",
    "              ax_min = min(ax),\n",
    "              ax_max = max(ax),\n",
    "              ax_median = median(ax),\n",
    "              ay_mean = mean(ay),\n",
    "              ay_sd = sd(ay),\n",
    "              ay_min = min(ay),\n",
    "              ay_may = max(ay),\n",
    "              ay_median = median(ay),\n",
    "              az_mean = mean(az),\n",
    "              az_sd = sd(az),\n",
    "              az_min = min(az),\n",
    "              az_maz = max(az),\n",
    "              az_median = median(az),\n",
    "              aT_mean = mean(aT),\n",
    "              aT_sd = sd(aT),\n",
    "              aT_min = min(aT),\n",
    "              aT_maT = max(aT),\n",
    "              aT_median = median(aT),\n",
    "              y = mean(y)\n",
    "             )\n",
    "    features = rbind(features, res)\n",
    "}\n",
    "\n",
    "#view all features\n",
    "features$y = as.factor(features$y)\n",
    "head(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create sample size for training the model\n",
    "From the information, we know that we have four states in the data. Based on this information, the data is split into train and test samples. The train set is used to build the model and test set is used to validate the model. The ratio between train and test is 80:20. You can adjust this based on type of data. The below table shows the number of observations for each group.   \n",
    "\n",
    "Note: It is adviced to have atleast 30 samples for each group. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-10T19:23:47.823447Z",
     "start_time": "2019-01-10T19:23:47.767Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  1   2   3   4 \n",
       " 67 109  93  93 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(features$y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above results, we can observe that there are atleast 30 samples for each group. Now, we can used this data to split into train and test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-10T19:25:44.315656Z",
     "start_time": "2019-01-10T19:25:44.255Z"
    }
   },
   "outputs": [],
   "source": [
    "#create samples of 80:20 ratio\n",
    "sample = sample(nrow(features) , nrow(features)* 0.8)\n",
    "train = features[sample,2:21]\n",
    "test = features[-sample,2:21]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Learning\n",
    "\n",
    "### Fault Classification using Keras\n",
    "TensorFlow™ is an open-source software library for Machine Intelligence. The R interface to TensorFlow lets you work productively using the high-level Keras and Estimator APIs, and when you need more control provides full access to the core TensorFlow API\n",
    "\n",
    "The Keras API for TensorFlow provides a high-level interface for neural networks, with a focus on enabling fast experimentation  \n",
    "\n",
    "The Estimator API for TensorFlow provides high-level implementations of common model types such as regressors and classifiers.  \n",
    "\n",
    "The Core TensorFlow API is a lower-level interface that provides full access to the TensorFlow computational graph.\n",
    "\n",
    "#### One-hot encoding on labels\n",
    "The labels on the data needs to be one-hot encoded for a keras model to be trained. There are a lot of advantages of doing so in training and you can read about it. Here, for one-hot encoding, we will be using caret package and dummy function as shown below. Then we will spit the labels as per train and test. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>y.1</th><th scope=col>y.2</th><th scope=col>y.3</th><th scope=col>y.4</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " y.1 & y.2 & y.3 & y.4\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 0 & 0 & 0\\\\\n",
       "\t 1 & 0 & 0 & 0\\\\\n",
       "\t 1 & 0 & 0 & 0\\\\\n",
       "\t 1 & 0 & 0 & 0\\\\\n",
       "\t 1 & 0 & 0 & 0\\\\\n",
       "\t 1 & 0 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| y.1 &lt;dbl&gt; | y.2 &lt;dbl&gt; | y.3 &lt;dbl&gt; | y.4 &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | 0 | 0 | 0 |\n",
       "| 1 | 0 | 0 | 0 |\n",
       "| 1 | 0 | 0 | 0 |\n",
       "| 1 | 0 | 0 | 0 |\n",
       "| 1 | 0 | 0 | 0 |\n",
       "| 1 | 0 | 0 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "  y.1 y.2 y.3 y.4\n",
       "1 1   0   0   0  \n",
       "2 1   0   0   0  \n",
       "3 1   0   0   0  \n",
       "4 1   0   0   0  \n",
       "5 1   0   0   0  \n",
       "6 1   0   0   0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# perform one-hot encoding\n",
    "dmy = dummyVars(\" ~ y\", data = features)\n",
    "onehot = data.frame(predict(dmy, newdata = features))\n",
    "\n",
    "# view the top data\n",
    "head(onehot)\n",
    "\n",
    "# split the labels\n",
    "trainY = onehot[sample,]\n",
    "testY = onehot[-sample,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Keras, you assemble layers to build models. A model is (usually) a graph of layers. The most common type of model is a stack of layers: the sequential model.  \n",
    "There are few things to notice here:    \n",
    "  - Layer dense is the densly connected neural network later  \n",
    "  - Unit is the neurons in the neural network  \n",
    "  - input_shape is the number of predictors  \n",
    "  - Layer_dropout applies Dropout to the input.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "dense (Dense)                       (None, 60)                      1260        \n",
      "________________________________________________________________________________\n",
      "dropout (Dropout)                   (None, 60)                      0           \n",
      "________________________________________________________________________________\n",
      "dense_1 (Dense)                     (None, 50)                      3050        \n",
      "________________________________________________________________________________\n",
      "dropout_1 (Dropout)                 (None, 50)                      0           \n",
      "________________________________________________________________________________\n",
      "dense_2 (Dense)                     (None, 4)                       204         \n",
      "================================================================================\n",
      "Total params: 4,514\n",
      "Trainable params: 4,514\n",
      "Non-trainable params: 0\n",
      "________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "## create your model,and add layers \n",
    "model <- keras_model_sequential() \n",
    "model %>% \n",
    "  layer_dense(units = 60, activation = 'relu', input_shape = ncol(train)) %>% \n",
    "  layer_dropout(rate = 0.2) %>% \n",
    "  layer_dense(units = 50, activation = 'relu') %>%\n",
    "  layer_dropout(rate = 0.2) %>%\n",
    "  layer_dense(units = 4, activation = 'softmax')\n",
    "\n",
    "## see your model structure\n",
    "summary(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "compile function configures a Keras model for training. Here we, load what model to use, type of loss, metrics and optimizers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "compile(model, loss = \"categorical_crossentropy\", optimizer = optimizer_adam( lr= 0.0001 , decay = 1e-6 ), metrics = \"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the model can be trained with different epochs, batches and split size. From the trained model we can notice that trained accuracy validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trained on 231 samples (batch_size=32, epochs=2,000)\n",
       "Final epoch (plot to see history):\n",
       "        loss: 0.1193\n",
       "    accuracy: 0.9567\n",
       "    val_loss: 0.2106\n",
       "val_accuracy: 0.931 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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eJXX3110vHz\nzjvv888/N5xY7HYuo6X05Oezdu3ayy67LLIjk82G9nZwHExmGASERHgjHUIZhmEYxsixVkHo\nnRDnFaUDLW2KqsaajK6QeHlh/sz8nj5JfqZ0T5s9evXq1eE2owBkWX7uueeWLVvW2trao0eP\nX/3qV/njxj/V3Pq+yy2qJ/yAJ6ryHeUHb6yqiJa/7SeoGU3SgMHioGLNYj1j14ScRbSUnujR\nUlmiR0vpiR7ND0SPltITPVpKT/RoKT3p1LgHH3zwVL6f5/nLL7/cZrM1NTW5XC6e53v37n3X\nXXc999xznSgVBRAMBrvSnnrhX2SpWf45okePHoMGDVrywQdQFGgaJAl2O6JjIMsIBhEdjW/e\ncmgMIyuKmecPt7a7QmJBQtyA5MSgLE/L7fHc5HExJuNbBw6XO10VtXUbNm2aMmWK2WxevXr1\n5MmT927edEt+j8tzs6qra/7xyiJvY+OjU6dcmZigQjsYDHVE/n6G/SI++fke+Q6TudDjipIl\nAIwic7XVwq7tXHubmpComS1n6TqRM4Te6BI9QRA0TetKHw2SU2GxWDRNo/mBhNH8QPQoCCN6\ngiAAoPmBhJnNZoZhutj8YLHQ++JfilOtGO0yqGKU/NyWLl168803g2WhaTBbEAwAQFQUfD6w\nLExmmEzIyoLTiYZ6uN39kxK+bmnbdPWcyW8ve3/mBYWJ8UMXvZ0RZY8xGSvaXc1+P2ezL1q0\naPbs2aokbbx6jonjZy79JCApfZLiy9qcAZP5tddeGzhwoEOWX3R5Fjpa/CdG/wZolzbW/Pbo\ngQKvbvdhhpF75Iol5ykpaWf28pAzhypGiR4F5USPKkaJHs0PRI8qRokeBeVEjypGSad2qj1G\nCSE/0syZM5OSki655BKZYeH3AYDVCkWBLMNuh90OWUJ1Faw2GIxgmEq3R1bVaKMxpCh+SX54\n47YUmzXcb9QnSvEWc1NLy6xZs4qTE12hUJ/EhOGvvTuiW9ozk8YaOE7VtD9u2HzDDTds2rQp\n2Wx+LLPbnUkJ/6pvfKXN6VYi8agI5o2UzDdTMqe0Nd199OsRbc0AoGl8RRlfUaakZ4RGjFG6\nZ5+9C0YIIYQQQgghhBDyM/opu9IzP9ppHy4hndrIkSNra2sL83IR/ukIiQgGwbJgGHAcYuPQ\n3IzGRrQ0Q9OcwRDHsn/dvEPVtAU7dm+sqW/0+U08NzUn64GRwwKSzDKMKIr58bEhRdnb1HK4\nte3JCaMNHAeAZZg/n1cSbG3ZtGlT+KETeP7+5MR9+TmPpialC0LHkDRgRVzS+OJxw0ZPfSst\nS/nmx5arq7G894Zl8SK+ogxUV04IIYQQQgghhJAu56cEo4SQn4zjuHXr1n22ciXDMFBkKApU\nFaEQqqtwrAIGA0JBREeHo1JFVV/bf9AiCPubWsranbVuj8BxIzPSn9y+q2dstKZpHMNYeL7a\n5Vl1rDLaZLTqEk+OYdLtttbWVv2jW1n2pvjYHXnZz6SnFJiM+pv2WOzX9ysuGjf9may8AMdF\n7qSuxrz0HctrLwqH9kNRzsD1IYQQQgghhBBCCDkzfkowqv1op324hHQNgwYNGjZs2KiMNKgq\nWBYhEZoGoxG8gKhoMAw6Is6ExHaebxFFSVEA5MXF/H1LaarN6hYljmVZhlm07+Btg/v/ZfOO\nFn+grO3brnBNPv+R1vaePXt+99EFhplmFN6zmZb3yJxkt+lvOm4wzS8YkDf+okdyC9uEyP5p\nXLPDtHyZ7YWnDJvWM9RIiBBCCCGEEEIIIV0CVYwScnb885//3NXqnJSdGcXz0FQA0DT4vHC2\nIxAAx0PTEBsHjkMwCIZRNQ3A181t1W5PebuzzuPJjom6qk/Bbwb3e2rH7uyYKAPHzv1w5Za6\nhqCs7HE0X7Ls01Hjxg0cOPCkxz127NicOXOysrL69Okzb/CgqRvXfdEza05MNKfrfdHM8Y/0\nLModN+Pu3gNqTJHN+Bif17h5vfXFp4xfrGLdtK8XIYQQQgghhBBCOjfuwQcfPNtjOCcEg0H1\nxD27O7XwLqKhUOhsD4T8oPj4+FmzZh1odEiq2rNnT4vF0tLYCABGIzgefh/i4sALCAbAMDAa\nAQaqEpAkjmWtgjA6s9v2+saVl108uUfWnIK8bnb72O4ZS8vKX9538K+bt7+874Bis5tMpq1b\nt3IcZzQaJUkyGo0ej2fatGl5If/bM6b8YcTQbIv59y8uHJiTM3/Y0JkxdknD4WCoY8G8xDA7\nYuKfz8ort0Zl+73JYhAAoyhcQ51h9w62pUm1R2v2qLN2BclPRbsMEz1BEDRNk2X5bA+EnBMs\nFoumaTQ/kDCaH4ge7UJO9ARBAEDzAwkzm80Mw3Sx+cFisZztIZAzhKEF72FOp7MrTesJCQmS\nJLlcVNbXmYwdO/brgwcBQBAgirDbIUowCOB5aIDLCVUFw4S3QuoeZa/z+jzzbw3XeYYUZdaS\n5aUNjoKEuO31jVkxUcedbk3TLILgk6Tw/ffp06ekpGTz0iVbrrmUZyPV4q/sPfCXA2W7d+8O\nf9mqKC+3tr/c5myTT+4oOtzVdtvxwxc11nK6SUNJThUHDpV79wFL5eedRkJCgizLTqfzbA+E\nnBMoKCd68fHxqqq2t7f/91PJLwDND0QvLi4OQFtb29keCDknUFBO9GJjYxmG6WLzQ0JCwtke\nAjlDKMsg5FyxatWqoYMHQ1Uj2xz5/AgGIMtQNQT8sFhgtQII78tUJcmypmU98/KKisq71qzP\nff7VRp/v6xuvcofEi/J7Vrs8qTbr3cUDVU17Ycp4xx03V916/XgT/+qrr47ISOtIRXc7mqtc\n7tra2o7N6+M57t6khF15PR5JTepmEPTD2xIdd0X/kj5jpz2TlefmIzdxjgbzyo+sC58xbNvE\nhOiNEyGEEEIIIYQQQjoNCkYJOVcYDIZPP/30pZde4gFoGhgGDAOWhSwhFIIsIxSCICA5BTwP\nUQTDOPz+mUs+eWP/oSaf/6FRw/2yfKClVVIUjmX/fN7wf5Xu/d3wwdf06R1tNCRbLY+NHZli\nMjZ4fOGHm792w5g339/e4JiQlTl39uzf/OY3HfXjVpa9OT52e272c91Se5+4ef0xo3l+wYCc\n8TPuKRhw3GwNH2RdTuOGtdYXnjKu/Yyh9qOEEEIIIYQQQgjpDPizPQBCyAkuuuii6dOn33XX\nXe+//76kafD7I5vUm0xwuWA2w+0GxyPc+cFkgih6JQmARxQ/P14NoMkfCMryjgaHomkju6WF\n79YVEg80tzT7/B+XH1tZUekWxbcOHN5yzWW9E+LqPN4Pyyr+vGzZo8nJV1111bJly9atW1de\nXu7xeLp3737jjTf2vHjmwjbXpx6v8k1y6mG5p7Pynu2eO6at+dbKI1ObGxhNY0Ihw67thr07\npZ750pDhSmr62bmChBBCCCGEEEIIIT8C9RiNoB6j5BxUWVk5atSooChC02AyIRBAdAxkCYoK\nSQTLwm6H2wNVgaoCYBnGyHH9khN2NjZFG42yqi6YMGZYespvVn8ZzkwHpiR93dyiarAZhNsG\n9f/DiKEL93x975dfZUVH2QyG3Y4mFUy61SKp6h9HFPdOiNvf3Prwpm3X/PrWe++9t0qUXm93\nvdbmdCkntx/t63XdXFl2eX2VWXeT0j1bHFQs98iFbr97ci6gHqNEj3oIEj3qMUr0aH4getRj\nlOhRj1GiRz1GSadGwWgEBaPk3OTz+fr37+90ucEyUDUIPEQRRiMsVhgEBAIIhSKbMokiWDac\nkDIAyzC9E+O9IZHn2HiTaWdjk6SqRo57+6KpBfGxU9798N5hg/smJYxbvGTxjCkX9syu93qL\nXnrjwVHD/+/LjZuuvrR/cmJ4ANvrG8e+vXT37t2pqakAvKq61On+d2t7eUg8aaiJknhNzbFb\nqo6mB/0dB9XoGKnfILHvAJhpU79zBQWjRI+CD6JHwSjRo/mB6FEwSvQoGCV6FIySTo16jBJy\nTrNarR9//DELDYoCloEsg2XBsvD74XBAFMELMJkQGwtBQEICTCawLMswqTbr0bZ2nmMVVTvu\ncgNgGSbGZJyak5UdEz0yI319de0DX225pFfuhT2zAaw6VpUbG5sRZc+KiepIRQEMTUtJtZj3\n798f/tLGslfHxWzqmf1m9/TRNqu+FrRZMPyzR6+CMRdc0b9kW2zkX5Fw+1Hb80+ZVi/nWprO\n1GUjhBBCCCGEEEII+S8oGCXkXNerV6/CoqLLe/campTIqCpUFcEgVAUsB4MBAT9ECW43GBZu\nNwBomqJptR6vBqai3ZVutzl8/l7xcb3i49RvKsSLEuPfPVS2rqq2e7Q9fGRDdV2c2WTmOa8o\n6R9d1TS/JHu93tLS0o7PAFkGk+22D7K6re2ZdVlstEG3WF5k2CWpGaOHjR9fPO6DlAyJZQEw\nsiTs3WVZ9LzlndeFskPhslZCCCGEEEIIIYSQs4iCUULOdQzDvPDCCxu9/ia//4Kc7DS7DZoG\nWYaqQJKhqjAZYY+CyQhBgKpCEMI72ocYRtW0r2rqAPhl6V+TxjT7A0uPlFe7PQ9v3HZxfs8o\no2FbfSMAWVU/KT+2q9GRFxcrKeqifQfCD60B1yxf5RTFm2++edb0aQUFBbfffrvP5+sYWx+T\n8en0lL35OX9MTkgTTtjMbVNc4pUDSnLGTv9Dfr/qb/av52oqTR+9b3t+gWHTeibgByGEEEII\nIYQQQshZQj1GI6jHKDnHBYPBFStWVFRUpKenT548+U9/+tO7774LABwHjkN41yOTCRwHrxc8\nD1UFx0EQ4PWBATTNyHEcxyqKmhsXaxH4Wwb0fWzrjmZf4Lp+hedlpk9//+NJ2d2b/f6pOdn/\n2LYz2Wpp8QeCssyx7KTszGcmj02z2Q62tF336epeY8c//fTT3x2hqGlLne4XW9v3B0Mn3cQB\nFzbW3VRTPq7VwXwz52gcJ/fMFwcVq+kZP+ulIyehHqNEj3oIEj3qMUr0aH4getRjlOhRj1Gi\nRz1GSadGwWgEBaOk0/H5fKNHj66qqops+24wgufg88FmgwYEAzAYIEmw2eDxQNWgqQyQGxdb\n3u6clN39b2NHDlq0+MUpE/65rfRgSxuAAzde/e9de988cNgZDI3tnpETG/3x0WOyopb96lqb\nQZBUtaLd2eIPTHxn2fPPP3/w4EFFUYqLiydPnsycuO/8Zp//pTbnSrdX+c700tPnvbG6/Oq6\n47HSt3s3Kcmp4sChckEROO5nv2qEglFyIgo+iB4Fo0SP5geiR8Eo0aNglOhRMEo6NQpGIygY\nJZ3Ul19+ecstt7S2tgKAyYRgEDYbvF7Y7OBYCAYEAjCZ4GyHqkLTwDDQNABxZlNhQtzRNtcf\nRw59dNN2I89Nyu6+YOKY53bufXHP/s8uvXjAK2+Nz8qs83jXXTH7ttVfvr7/kKQoGsAANoNh\nSk4WxzKrjlX1H17y1ltv8Tx/0sAcsvxuu+uVNledJJ10k1FVL3TUXV9bMa7F0XFQs9qkon7i\ngCGaPernvWS/eBSMEj0KPogeBaNEj+YHokfBKNGjYJToUTBKOjUKRiMoGCWd2saNG2fNmqWq\nKhgGvABZgskERYEkwWaHLEEUoWrgObAsFAWy3JGQcgyjaNpDo4Y/Vbq7Z2yMT5LGd8+odLm/\nqKq5pk/vZUcqsqLtB1pan58yYWpO1t1rNnxcfmzTVXMyouwA2gLBUW++d9mvb7vzzjv149m2\nbdvSpUtbWlryCwp6XH7FO6Kywev77lwz0N1+fXX5ZfVV1nArAAAcJ+X2kvoPUrp1x4mFqOR0\noWCU6FHwQfQoGCV6ND8QPQpGiR4Fo0SPglHSqXEPPvjg2R7DOSEYDKpdaKfs8C+yodDJrR5J\nV5WZmXn55Zd/9NFHXo8HPAdVhSyDZcGyMJkQCIDjYDaBF6AqAGA2g+OQloZQSFNVaNrXza3b\n510ekuXdjiaLwH9UVjEtN6fFH6j1eI+2O/8x7rxLC/LmLFuxrKz8t8UDL+iZHX5cs8ALLPvS\n+o2pqakcx4V/Y16wYMFdv7mtQAz0VMSNGze+/eKLL196yU052RaOKxPFoPptQNpgNK9ISn+h\ne26lxZbt9yWKIWga19IsfL1XOHyQURU1Nh6CcDauaFdGb3SJniAImqZ1pY8GyamwWCyaptH8\nQMJofiB6FIQRPUEQAND8QMLMZjPDMF1sfrBYLGd7COQMoYrRCKoYJV3Dhg0bbrrppsjKegAM\nA6MRwSDs9simTBwHUQTPw2SCBvi80AAt8qmAxSAMSU3eXtfIMszeG64csmixmRcavN7t8+Z+\nWn78ja8PxZlMVxYV3DygT/j89dW1l3+0UtMQazZVOl0FhYWzZ89+7JGHV102szgtJXzOnWvW\nb1XZzz//HIBPVT9wuhe2tB0WT15fz0Ab1+K4ubp8alM9f+IGTVLfgUr3bCogPV2oYpToUVBO\n9KhilOjR/ED0qGKU6FFQTvSoYpR0auzZHgAh5HQ677zzDh8+fPz48U8//bRv377QNASDYBhI\nElgWvBDpNBodA15AKAiOAwPwPOxRYBi/KK2vqg3Isl+SWgKBlZde7AyFLIJQ7fJ8cPjo74YN\nGZCS9NmxyvBjbaypm7lk+a2D+r8+fXJbIJgbF5sR8P7j0UdHdEvrSEUdPv9dQwfs2bOnqakJ\ngJVlp0H1XTY7///mDyrdyuvaj2pg1iakzBk4Mmfs9D/k96uw2AEwiiIcOWh5/03Lqy8Iu3cw\nIXpvRgghhBBCCCGEkNODKkYjqGKUdElHjhy54IILXC4XBAGKAlWD1QKfDzY7/D4IAjgeAAQe\nHg9YFpqGhAR4vAgGoKomngOYGKMh2mT0hMRnJo/Li4sZ9MriuYX5kqotPnB4SGry+xdf0OuF\n1/4wovi3xQMf2bjtb1tLp+RkvX/xBc/v2vfI5u0t/gDPsrKqrly5cvDgwQBuu+22tu1bPpo9\nnWWYZoPp2eRu/0hKV5JSTho5o2mj25vm1Ryb0VhnUiMdSDWOl3vmSX0HKlk9zuyF7FKoYpTo\nUUUY0aOKUaJH8wPRo4pRokcVo0SPKkZJp0bBaAQFo6QLe+aZZx5++GFV0yK70ofXpKsqbHYE\n/GBYCHxkO6aoKLjdUBRwHCQJmsYAAstajQZ3MHRp7/yPyyrMAq9p8EnStNxsA8e3+P3HnO59\nN1y5saZu2vsfPzK65JFN2+YXD358+86JWZk7Ghz1Xq+B46ISk7Zu3Wo0GgsKCl4ZWzIpu3vH\n8O7bsGXb0BHx11z3qcerfGdGipbF2Q21N1Uf7ef+NshTklOlfoPk3kWaYDgjl7BLoWCU6FHw\nQfQoGCV6ND8QPQpGiR4Fo0SPglHSqdFSekK6vt/97ndtbW3PPP30lClTrBYLVBXh/euDQTAs\nWAYAJAl2O4JBsCyMRjAMBAEMo3GcqKrtgaCiaYsPHA4qygczL2wPBucU5BWnpe5xNK06Xp1q\ns26urb/0wxUz8nJWH6/yitIDGzb3S0rcVt94X8mQVZfNfHDUMH9Ly+233w5AlmVTuEz1G2aW\njT1W8XJm2rbcrDviYlKFE2518YaXM3oUj5g8avjERRk9PDwPgHM0mFYvtz73hGn1cq7JceYu\nJSGEEEIIIYQQQroKqhiNoIpR0oVZrVZZlkOhUPjLysrKa6655uDBg+A4AFBVmM3w+xEVBa8X\nvACjEdDg94NhoGkQBCgqBB5+PwAGYFn21wP7/n74kB7PvRJSlBijUdG0zCi7ReCdodCiqRNH\nvvm+geN2zrs8Ny4m/KCfH6++aOnyffv2TZs2bZTV9MKU8eHjQVkZ9to7k664sqamZvXq1cFg\nMC+/14yHHjrYI3elxyt9Z4IyqcoFjvrrayvGtXybh6rpGWKf/nJ+oWagAtL/jipGiR5VhBE9\nqhglejQ/ED2qGCV6VDFK9KhilHRqFIxGUDBKurCTgtGw8vLy888//9sXCc9D06AosNqgyAiF\nYI9CwA9VhcEAUYTBAFmGbrsknmVVTQMQbzaVpKdVud0HW9penDKh0uV68Kutg1KSNl19acfJ\nGpC84IXhY8ce3rLF4ffNzM+9KC+nNRB86KutzcGgoiiTe3T//fAhsSbjyorKhzdte/n1N/qO\nGfOhy/Nmu+tQ8ISRh+X73FfVHr+29niCGLlVEwxyfm+p7wAlPeN0X8IuhYJRokfBB9GjYJTo\n0fxA9CgYJXoUjBI9CkZJp0bBaAQFo6QL+95gNGzr1q033nijw+GITAUcB02L/N9ogs+LqCiw\nLNxucBxUDWYzJAkch4AfADQt1Wb1SdK8PoXP7dqratrHc2bcuXpdSFFMPL/vhivDj+Lw+R/e\ntO3lvQc4hnn/4gv+vHHrbkczAIZBflysR5TsBsO2ay8zhitYgad27H6ppnHLli0ANGCzz/9m\nu+sTlyf0nfnKqKozHLXzaipGtzaziNyqxsZLBUVSn/5aVPTPcDk7PQpGiR4FH0SPglGiR/MD\n0aNglOhRMEr0KBglnRr34IMPnu0xnBOCwaCqqmd7FKdN+BfZ7w3CyC+QwWBQVVVRlO/e1K1b\nt1tuuWXw4MHx8fFOp7O9tTWyfD7chDS8C5PXC7MZvABJhKpCA+KmMMXVAAAgAElEQVTjoanI\nzATLeZ1OjmUbvN4Ykyk7Jqq0wbGvqeU3g/t/WFaRHx/bLcr+wPrNV36yimOZgCR3s9uWlpWP\nykx/56KpY7p3W1FeKaqqTRAmZne/oGd2x6jizKZH16y74447eJ5ngEyDcGGU/Yb42O4GoUGS\nm+Rvn4jCMAfs0W+lZy/K6FFtsaQFA8liiAkG+Joqw+4dnKMBgqDGxEX2myIA6I0uOZEgCJqm\ndaWPBsmpsFgsmqbR/EDCaH4gehSEET1BEADQ/EDCzGYzwzBdbH6wWCxnewjkDKGK0QiqGCVd\n2H+oGNUTRbFbt26TsrqvOl4JABwHhoUiAwDPg2UhioiNg8UCgwF+P5xOKApkCSdOI9FGY//k\nhKv69L55xRowDMcw/ZMTShuaMqPsLYFgn8T4lZdd/PDGrf/asSfJaukeHbXX0TwtN/vVCyeH\nv90nSWuOV1+9cm11dTXLfs8Gcbv8wbecrqVOt/f7Pswobm+5sr5qdkN1rCSGj2hWm5TfW+47\nUElM+h+vXNdEFaNEj4JyokcVo0SP5geiRxWjRI+CcqJHFaOkU6OK0QiqGCVd2H+oGNXjOO6z\nzz47Ly5qeLfUSqdb01RRkiIFpAB4HrIMjkN7OwQeoSBEEQygKGBZmM2RJqSCISRJVS73rsYm\nvywDiDEZm3wBE8d9cfns53btvaQgd9WxqmVHKiyC4AqGDByXFxe7trJmfFYGgOtXfH7Dp5+/\nf/gowzCCIAwdOpTRFXs2NDSsXr26etfOiTbLn/sUFpqNtU5XA6AvCK0zW1YmpT2Tlb8jJp6H\nluv3cmKIa6gT9pTyFWUAo8XF45s1+79M9EaX6FFFGNGjilGiR/MD0aMgjOhRxSjRo4pR0qnx\nZ3sAhJBzyN///vfp06df3Tsv1mwSVXVSdub+ptZyp0tTVQSD4Dj4A1AVuN1gWRgMCAbBMOAF\ncDw0DbwAgYckAqj1eMP32ewPABialpwZbZ+YnVnR7vqwrCLdbhualvzJ0eNlbe0vTh2/prJm\nwuKlJp4fnp666/or0u22jTX1tz61gGXZ2bNnP/roo+vXr29ra1MUpVdcTLTR+CdH8/jzz//L\nX/5SN/eSywb2d44Zt7FXoTcqpuOJhFh2RVLaiqS0JDF4WV3VlXWVfT1OztHArV6urf1M7pk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OplGydEFT3UWO2olNDosqgxfYvF5s3/5c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\nhFvGE+jRKBw6P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bAKuV1dN8XSfOmz14W9/QAAPAZjQCHzZXIiDjss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\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/H9VkBAdDQePkTb9shIh1aL/oPw8UK0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\n+rjjWptVo1J4M6xeLtPSlD/gJZPpZVRQRLj3k0yDQl6ZJHRlT2V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LFi3q06fPuXPnxo8fn5ubKz1lipo4ceJbb70lKkLu2rVr3rx5ou0k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\nnkKBymTSmMxTTpwDwOVyR4wY0TM8/Kt1a1t5Kua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o5CqUzqjkS5TOqPcDpTMq+RKlMyr5EqUzKvkS5S+Nvnr16nc9hj8FmUym+bfHnt4d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\n/ewOGrrUx0P582BzU6+DJ1au/SAhIWHkuStyudzb2zs8PNz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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train the model\n",
    "history = fit(model,  data.matrix(train), data.matrix(trainY), epochs = 2000, batch_size = 32, validation_split = 0.2)\n",
    "\n",
    "# show trained information\n",
    "history\n",
    "\n",
    "# Adjust plot sizes\n",
    "options(repr.plot.width = 15, repr.plot.height = 6)\n",
    "plot(history)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can evaluate the model with the test data. The loss and test accuracies can be as seen below. Neural networks using keras package is one of the methods that we can use to train our fault classification mdoels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$loss</dt>\n",
       "\t\t<dd>0.121048261449762</dd>\n",
       "\t<dt>$accuracy</dt>\n",
       "\t\t<dd>0.945205450057983</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$loss] 0.121048261449762\n",
       "\\item[\\$accuracy] 0.945205450057983\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$loss\n",
       ":   0.121048261449762\n",
       "$accuracy\n",
       ":   0.945205450057983\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$loss\n",
       "[1] 0.1210483\n",
       "\n",
       "$accuracy\n",
       "[1] 0.9452055\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model %>% evaluate(data.matrix(test), data.matrix(testY), batch_size = 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### References\n",
    "\n",
    "[1] Amruthnath, Nagdev, and Tarun Gupta. \"A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance.\" In 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), pp. 355-361. IEEE, 2018.\n",
    "\n",
    "[2] Amruthnath, Nagdev, and Tarun Gupta. \"Fault class prediction in unsupervised learning using model-based clustering approach.\" In Information and Computer Technologies (ICICT), 2018 International Conference on, pp. 5-12. IEEE, 2018.\n",
    "\n",
    "[3] https://tensorflow.rstudio.com\n",
    "\n",
    "[4] https://amunategui.github.io/dummyVar-Walkthrough/"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
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   "file_extension": ".r",
   "mimetype": "text/x-r-source",
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